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Introduction:

The Mood-Based Music Recommendation System is a web application that aims to enhance users' mood by recommending music based on their emotional state detected through facial expressions using OpenCV and a pre-trained emotion model. This project utilizes a combination of Python, Flask, Convolutional Neural Networks (CNN), OpenCV, K-Nearest Neighbors (KNN), and Standard Scalar to create a personalized music experience for users. The system has been designed to increase the mood level of users by up to 25%.

Key Technologies Used:

  1. Python: The core programming language used for building the application.
  2. Flask: A Python web framework for developing the web application.
  3. OpenCV: Open Source Computer Vision Library used for face detection and feature extraction.
  4. CNN: Convolutional Neural Networks are employed to predict emotions from facial expressions.
  5. K-Nearest Neighbors (KNN): A machine learning algorithm used for music recommendation.
  6. Standard Scalar: A preprocessing technique for feature scaling in the KNN algorithm.
  7. YouTube: The platform for playing recommended music videos.

Project Details:

1. Face Detection and Emotion Recognition:

  • The application uses OpenCV for face detection, which captures the user's facial expressions through a webcam or uploaded images.
  • A pre-trained emotion model based on a CNN is used to recognize the user's emotional state based on facial expressions.

2. Mood-Based Music Recommendation:

  • The emotion detected from the user's face is then used as input to a KNN algorithm.
  • The KNN algorithm, along with Standard Scalar preprocessing, recommends a list of songs that match the user's emotional state.
  • Song titles and relevant details are generated based on the recommendation.

3. YouTube Integration:

  • The recommended songs are played on YouTube to provide an immersive music listening experience.
  • Users can interact with the application by liking, disliking, or skipping songs to further tailor their recommendations.

4. User Mood Enhancement:

  • The personalized music recommendations are designed to enhance the mood of the users.
  • The system aims to increase the user's mood level by up to 25%, providing a positive and enjoyable music experience.

5. Integration with LinkedIn Projects:

  • The project can be integrated with LinkedIn Projects to showcase your skills and accomplishments in creating this innovative music recommendation system.
  • You can provide relevant details about the project, including its purpose, technologies used, and its impact on user experience.

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